Classification, Clustering and Data Analysis

Bys: Jajuga, K. and Bock, H. -H.

The present volume contains a selection of papers presented at the Eighth Conference of the International Federation of Classification Societies (IFCS) which was held in Cracow, Poland, July 16-19, 2002. All originally submitted papers were subject to a reviewing process by two independent referees, a procedure which resulted in the selection of the 53 articles presented in this volume. These articles relate to theoretical investigations as well as to practical applications and cover a wide range of topics in the broad domain of classifi- cation, data analysis and related methods. If we try to classify the wealth of problems, methods and approaches into some representative (partially over- lapping) groups, we find in particular the following areas: - Clustering - Cluster validation - Discrimination - Multivariate data analysis - Statistical methods - Symbolic data analysis - Consensus trees and phylogeny - Regression trees - Neural networks and genetic algorithms - Applications in economics, medicine, biology, and psychology. Given the international orientation of IFCS conferences and the leading role of IFCS in the scientific world of classification, clustering and data anal- ysis, this volume collects a representative selection of current research and modern applications in this field and serves as an up-to-date information source for statisticians, data analysts, data mining specialists and computer scientists.

• Format: Paperback
• Language: English
• ISBN: 354043691X
• ISBN13: 9783540436911
• Release Date: June 2002
• Publisher: Springer
• Length: 508 Pages
• Weight: 1.52 lbs.
• Dimensions: 1.1" x 6.2" x 9.2"

Customer Reviews

Understand clusters and clustering deeply

This is a good and broad approach about cluster and clustering. It is better for those who want to understand deeply the theme. Is has lot of formulas and mathmatics.

different methods for finding clusters

The book has a nice treatment of the problem of finding, in some sense, clusters in data. Several papers point out that there is often some subjectivity here, as to which data sits in a particular cluster. Fuzziness in the boundary of a cluster. It can depend on what your underlying model is. Possibly of interest to some is work on high dimensionality data, and trying to find clusters in these. Even visualisations might be non-trivial. The book has value in letting you see a variety of ideas for finding clusters. Perhaps some of these might prove germane to your research.